PD5182 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.
## Joining, by = "PDID"
patient ID age_at_sample_exact cell_type phase BaitLabel
1 PD5182 COLONY33 32.74196 BFU-E-Colony Colony NA
4 PD5182 PD5182dc 32.74196 PB Gran Recapture PD5182dc
5 PD5182 PD5182dd 32.74196 BM MNC Recapture PD5182dd
6 PD5182 PD5182de 32.74196 BM MNC Recapture PD5182de
7 PD5182 PD5182df 32.91444 PB Gran Recapture PD5182df
8 PD5182 PD5182dg 33.75496 PB Gran Recapture PD5182dg
9 PD5182 PD5182dh 45.00205 PB Gran Recapture PD5182dh
10 PD5182 PD5182di 46.03696 PB Gran Recapture PD5182di
2 PD5182 COLONY47 46.71321 BFU-E-Colony Colony NA
11 PD5182 PD5182dj 47.53183 PB Gran Recapture PD5182dj
12 PD5182 PD5182dk 52.38056 PB Gran Recapture PD5182dk
3 PD5182 COLONY53 53.38535 BFU-E-Colony Colony NA

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.0000   -0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  17.4279  0.4079  42.7263  <.0001  16.6284  18.2274  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
node driver status child_count type colony_count mean_lambda_rescaled correction sd_rescaled lb_rescaled ub_rescaled median_rescaled p_lt_wt
-1 WT 1 -1 local 110 17.42790 1.080409 0.1663798 17.15285 17.76982 17.40473 NA
170 JAK2 1 38 local 12 19.16355 1.080409 0.5749856 18.06987 20.32566 19.14924 0.000775
268 DNMT3A 1 10 local 10 17.95253 1.080409 0.3560822 17.26257 18.65970 17.94769 0.067225
101 9pUPD 0 1 local 1 21.43593 1.080409 2.6286205 16.92506 27.33643 21.18361 0.036400
155 2pUPD 0 1 local 1 21.61285 1.080409 2.4945804 17.55756 27.40046 21.29360 0.021925
182 1q+:9pUPD:JAK2 1 26 local 26 21.32762 1.080409 0.9864987 19.55895 23.42665 21.26544 0.000025

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient node driver child_count lower_median upper_median lower_lb95 lower_ub95 upper_lb95 upper_ub95 N group age_at_diagnosis_pcy max_age_at_sample min_age_at_sample
PD5182 268 DNMT3A 10 0.2359533 0.2589871 0.1407921 0.9406432 0.1519856 1.019314 12 DNMT3A 33.42916 54.11362 33.47023
PD5182 170 JAK2 38 0.1196292 0.6292411 0.0692699 0.2585890 0.2124202 1.859060 12 JAK2 33.42916 54.11362 33.47023
PD5182 171 9pUPD 37 0.6292411 18.4476081 0.2124202 1.8590599 17.4430488 19.441565 12 9pUPD 33.42916 54.11362 33.47023
PD5182 182 1q+ 26 20.3160206 33.6663171 19.3269330 21.2720933 32.1315144 35.419601 12 1q+ 33.42916 54.11362 33.47023

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates
label node het.sensitivity chr start end nhet nhom mean_loh_event lower_loh_event upper_loh_event t_before_end t_before_end_lower t_before_end_upper kb count_in_bin count_se pmut pmut_se xmean xse_mean xsd x2.5. x50. x97.5. xn_eff xRhat lmean lse_mean patient driver3 child_count
2pUPD 155 0.8461 2 23368 61755254 12 0 NA NA NA NA NA NA 61700000 10205 101.02 0.02229 0.0002206 0.09543 0.000562 0.0845 0.002647 0.07201 0.3137 22608 1 12.114 0.0008887 NA NA NA
9pUPD 171 0.9858 9 10469 46148517 3 2 9.542 3.539 15.34 8.907 3.112 14.91 46100000 7727 87.90 0.01688 0.0001920 0.49646 0.001052 0.1760 0.157042 0.49901 0.8241 27995 1 4.540 0.0003152 PD5182 9pUPD:JAK2 37
9pUPD_B 101 0.8073 9 10469 37654257 2 2 NA NA NA NA NA NA 37600000 7234 85.05 0.01580 0.0001858 0.59178 0.001164 0.1788 0.221389 0.60592 0.8932 23602 1 8.607 0.0006664 NA NA NA

Duplications?

## Timings using the Clade Specific Rates
label node het.sensitivity chr start end dupcount ndupcount mean_dup_event lower_dup_event upper_dup_event t_before_end t_before_end_lower t_before_end_upper kb count_in_bin count_se pmut pmut_se xmean xse_mean xsd x2.5. x50. x97.5. xn_eff xRhat lmean lse_mean patient driver3 child_count
1q+ 182 0.9839 1 125000000 300000000 3 2 31.26 26.34 33.62 2.436 0.07825 7.351 124300000 16092 126.9 0.03514 0.000277 0.818 0.0009285 0.1485 0.4507 0.8541 0.9942 25571 1 6.918 0.0003937 PD5182 1q+:9pUPD:JAK2 26

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots